Spatial water quality assessment of Langat River Basin (Malaysia) using environmetric techniques (original) (raw)
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The spatial water quality data (281x22) obtained from 12 sampling stations located along the Terengganu River and its main tributaries were evaluated with environmetric methods. Principal component analysis was used to investigate the origin of each variable due to land use and human activities based on the three clustered regions obtained from the hierarchical agglomerative cluster analysis. Six principal components (PCs) were obtained, where six varimax factor (VF) of values more than 0.70 that considered strong loading are discussed. The possible pollution sources identified are of anthropogenic sources, mainly municipal waste, surface runoff, agricultural runoff, organic pollution and urban storm runoff. As a conclusion, the application of environmetric methods could reveal important information on the spatial variability of a large and complex river water quality data in order to control pollution sources.
Jurnal Teknologi, 2015
The spatial water quality data (281x22) obtained from 12 sampling stations located along the Terengganu River and its main tributaries were evaluated with environmetric methods. Principal component analysis was used to investigate the origin of each variable due to land use and human activities based on the three clustered regions obtained from the hierarchical agglomerative cluster analysis. Six principal components (PCs) were obtained, where six varimax factor (VF) of values more than 0.70 that considered strong loading are discussed. The possible pollution sources identified are of anthropogenic sources, mainly municipal waste, surface runoff, agricultural runoff, organic pollution and urban storm runoff. As a conclusion, the application of environmetric methods could reveal important information on the spatial variability of a large and complex river water quality data in order to control pollution sources.
Pertanika Journal of Science and Technology
In Pasir Gudang, an accelerated industry-based economy has caused a tremendous increase and diversity of water contamination. The application of multivariate statistical techniques can identify factors that influence water systems and is a valuable tool for managing water resources. Therefore, this study presents spatial evaluation and the elucidation of inordinate complex data for 32 parameters from 25 sampling points spanning 20 rivers across Pasir Gudang, summing up to 1500 observations between 2015-2019. Hierarchical cluster analysis with the K-means method grouped the rivers into two main clusters, i.e., proportionately low polluted rivers for Cluster 1 (C1) and high polluted rivers for Cluster 2 (C2), based on the similitude of water quality profiles. The discriminant analysis applied to the cluster resulted in a data reduction from 32 to 7 parameters (Cl, Cd, S, OG, temperature, BOD, and pH) with a 99.5% correct categorization in spatial analysis. Hence, element complexity wa...
Environmetric Techniques Application in Water Quality Assessment: A Case Study in Linggi River Basin
In this research, determination of water quality status for Linggi River was carried out by using non-parametric Mann-Kendall analysis. HACA and PCA has been used to classify the river to obtain the clearest picture of the water quality status. The dataset includes six parameters for six monitoring stations (1997 to 2012). Mann-Kendall trend analysis shows significant improvement trend for all parameters studied except for BOD (WQ1 (P<0.1) and WQ6 (P<0.05)) and SS (WQ4 to WQ6 (P<0.05)). This indicates that even though the WQI getting good, a few parameters such as BOD and SS need to be watched and improved by the local authority to make sure the WQI continuously getting better in the future. HACA grouped the six monitoring stations into three different clusters based on their similarities namely less pollution site (LPS), medium pollution site (MPS) and high pollution site (HPS). HACA grouped one station (WQ1) into LPS, two stations into MPS (WQ2 and WQ3) and three stations into HPS (WQ4, WQ5 and WQ6). PCA was used to investigate the origin of each water quality variable based on the clustered region. Three principal components (PCs) were obtained with 75.3% total variation for HPS, 73.4% for MPS and 68.1% for LPS. The major pollution source for HPS are of anthropogenic source (municipal waste, domestic wastes) while for MPS the major source of pollution was from non point source pollution such as animal husbandry and livestock farms. For the LPS, major sources come from the sea tide effect (natural effect). The identification and classification of different region by this study will help the local authorities make better and more informed decisions about the improvement water quality program for the future.
Surface Water Quality Assessment of Terengganu River Basin Using Multivariate Techniques
Surface stream water is truly encountering sullying that undermines human wellbeing, biological community and plants/creatures life. The study investigates the spatial variation with the aim to identify the surface water pollution using multivariate statistical techniques. Thirty water quality parameters were extracted from 2003-2007 monitoring stations by Department of Environment Malaysia. The spatial variation of the water quality, identification of the prospective pollution sources and the explanation of huge complicated water quality data sets were assessed using multivariate statistical techniques which includes cluster analysis (CA), discriminant analysis (DA) and principal component analysis/factor analysis (PCA/FA). The revealed that thirteen sampling stations were grouped by CA into two major classes: Low Pollution Source (LPS) and Moderate Pollution Source (MPS) and each group show similar water quality characteristics. DA through standard mode, backward stepwise mode and forward stepwise mode rendered correct assignation of 83.03%, 81.55% and 80.81% with four significant variables (BOD, conductivity, NO3 and Zn) as the most significant. Indeed, DA reduces the data and produces good result for the spatial variation of the river. PCA identifies variables liable for water quality variation. Moreover, PCA revealed cumulative variance of 73.62% of the overall variance, each having greater than >1 eigenvalue. PCA suggested the major variations in river are attributed to domestic waste, agricultural activities and industrial activities this represent (anthropogenic activities) and erosion as well as runoff indicating (natural processes). Thus, this study demonstrates the usefulness of multivariate statistical techniques for analysis and interpretation of complex data sets, and in water quality assessment, identification of pollution sources/factors and understanding spatial variations in water quality for Effective River water quality management.
2015
Multivariate Statistical techniques including cluster analysis, discriminant analysis, and principal component analysis/factor analysis were applied to investigate the spatial variation and pollution sources in the Terengganu river basin during 5 years of monitoring 13 water quality parameters at thirteen different stations. Cluster analysis (CA) classified 13 stations into 2 clusters low polluted (LP) and moderate polluted (MP) based on similar water quality characteristics. Discriminant analysis (DA) rendered significant data reduction with 4 parameters (pH, NH3-NL, PO4 and EC) and correct assignation of 95.80%. The PCA/FA applied to the data sets, yielded in five latent factors accounting 72.42% of the total variance in the water quality data. The obtained varifactors indicate that parameters in charge for water quality variations are mainly related to domestic waste, industrial, runoff and agricultural (anthropogenic activities). Therefore, multivariate techniques are important ...
Multivariate Statistical techniques including cluster analysis, discriminant analysis, and principal component analysis/factor analysis were applied to investigate the spatial variation and pollution sources in the Terengganu river basin during 5 years of monitoring 13 water quality parameters at thirteen different stations. Cluster analysis (CA) classified 13 stations into 2 clusters low polluted (LP) and moderate polluted (MP) based on similar water quality characteristics. Discriminant analysis (DA) rendered significant data reduction with 4 parameters (pH, NH3-NL, PO4 and EC) and correct assignation of 95.80%. The PCA/FA applied to the data sets, yielded in five latent factors accounting 72.42% of the total variance in the water quality data. The obtained varifactors indicate that parameters in charge for water quality variations are mainly related to domestic waste, industrial, runoff and agricultural (anthropogenic activities). Therefore, multivariate techniques are important in environmental management.
Catena, 2017
In this study, cluster analysis (CA) and discriminant analysis (DA) were used to assess the water quality and evaluate its spatial and temporal variations in South Florida. For this purpose, 15 years (2000-2014) data set of 12 water quality variables covering 16 monitoring stations, and about 35,000 observations were used. Agglomerative hierarchical CA grouped 16 monitoring sites into three groups (low pollution, moderate pollution, and high pollution) based on their similarity of water quality characteristics. DA, as an important data reduction method, and CA were used to assess the water pollution status and analysis of its spatiotemporal variation. It was found by the stepwise DA that five variables (chl-a, DO, TKN, TP and water temperature) were the most important discriminating water quality parameters responsible for temporal variations. In spatial DA, the stepwise mode identified seven variables (chl-a, DO, TKN, TP, magnesium, chloride, and sodium) and six variables (DO, TKN, TP, turbidity, magnesium, and chloride) as the most important discriminating variables responsible for spatial variations in wet and dry seasons, respectively. Different patterns associated with spatial variations were identified depending on the variables and considered season, however the overall trend of environment pollution problems was found from the low pollution (LP) region to high pollution (HP) region. It is believed that the results of this study could be very useful to the local authorities for the control and management of pollution and better protection of important riverine water quality.
SPATIAL ASPECT OF SURFACE WATER QUALITY USING CHEMOMETRIC ANALYSIS
Journal of Applied Sciences in Environmental …
Chemometric or multivariate techniques were applied to identify the spatial variation and pollution sources of Jakara River Kano-Nigeria. Thirty water samples were collected: twenty three along River Getsi and seven surface water samples along the main channel River Jakara. Twenty three water quality parameters namely: , and calcium were analyzed. Hierarchical cluster analysis (CA) grouped the sampling points into three clusters based on the similarities of river water quality characteristics into industrial, domestic and agricultural water pollution sources. Forward and backward Discriminant analysis (DA) effectively discriminate five and fifteen water quality variables respectively with 100% each correct assigning from the original twenty three variables. PCA/FA were used to investigate the origin of each water quality parameters due to various land use activities, seven principal components were obtained with 77.5% total variance, in addition PCA identify three latent pollution sources convinces to support CA.